Overview

Dataset statistics

Number of variables24
Number of observations49865
Missing cells106448
Missing cells (%)8.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 MiB
Average record size in memory178.0 B

Variable types

Numeric8
Categorical14
Boolean2

Alerts

status has constant value "approved" Constant
trusted has constant value "0" Constant
recommendedFlag has constant value "0" Constant
isAnonymous has constant value "False" Constant
userDisplayName has a high cardinality: 20041 distinct values High cardinality
userLocation has a high cardinality: 8257 distinct values High cardinality
commentBody has a high cardinality: 49849 distinct values High cardinality
createDate has a high cardinality: 49803 distinct values High cardinality
updateDate has a high cardinality: 49793 distinct values High cardinality
approveDate has a high cardinality: 49237 distinct values High cardinality
parentUserDisplayName has a high cardinality: 10037 distinct values High cardinality
articleID has a high cardinality: 9448 distinct values High cardinality
df_index is highly correlated with commentID and 3 other fieldsHigh correlation
commentID is highly correlated with df_index and 3 other fieldsHigh correlation
commentSequence is highly correlated with df_index and 3 other fieldsHigh correlation
parentID is highly correlated with df_index and 3 other fieldsHigh correlation
permID is highly correlated with df_index and 3 other fieldsHigh correlation
df_index is highly correlated with commentID and 3 other fieldsHigh correlation
commentID is highly correlated with df_index and 3 other fieldsHigh correlation
commentSequence is highly correlated with df_index and 3 other fieldsHigh correlation
recommendations is highly correlated with replyCountHigh correlation
replyCount is highly correlated with recommendationsHigh correlation
parentID is highly correlated with df_index and 3 other fieldsHigh correlation
permID is highly correlated with df_index and 3 other fieldsHigh correlation
df_index is highly correlated with commentID and 3 other fieldsHigh correlation
commentID is highly correlated with df_index and 3 other fieldsHigh correlation
commentSequence is highly correlated with df_index and 3 other fieldsHigh correlation
parentID is highly correlated with df_index and 3 other fieldsHigh correlation
permID is highly correlated with df_index and 3 other fieldsHigh correlation
editorsSelection is highly correlated with userTitle and 4 other fieldsHigh correlation
commentType is highly correlated with userTitle and 5 other fieldsHigh correlation
userTitle is highly correlated with editorsSelection and 6 other fieldsHigh correlation
recommendedFlag is highly correlated with editorsSelection and 6 other fieldsHigh correlation
trusted is highly correlated with editorsSelection and 6 other fieldsHigh correlation
isAnonymous is highly correlated with editorsSelection and 6 other fieldsHigh correlation
depth is highly correlated with commentType and 5 other fieldsHigh correlation
status is highly correlated with editorsSelection and 6 other fieldsHigh correlation
df_index is highly correlated with commentID and 3 other fieldsHigh correlation
commentID is highly correlated with df_index and 3 other fieldsHigh correlation
commentSequence is highly correlated with df_index and 3 other fieldsHigh correlation
userID is highly correlated with userTitleHigh correlation
userTitle is highly correlated with userID and 1 other fieldsHigh correlation
recommendations is highly correlated with replyCount and 1 other fieldsHigh correlation
replyCount is highly correlated with recommendationsHigh correlation
editorsSelection is highly correlated with recommendationsHigh correlation
parentID is highly correlated with df_index and 3 other fieldsHigh correlation
depth is highly correlated with userTitle and 1 other fieldsHigh correlation
commentType is highly correlated with depthHigh correlation
permID is highly correlated with df_index and 3 other fieldsHigh correlation
userTitle has 49823 (99.9%) missing values Missing
parentID has 28290 (56.7%) missing values Missing
parentUserDisplayName has 28299 (56.8%) missing values Missing
recommendations is highly skewed (γ1 = 20.49125599) Skewed
commentBody is uniformly distributed Uniform
createDate is uniformly distributed Uniform
updateDate is uniformly distributed Uniform
approveDate is uniformly distributed Uniform
df_index has unique values Unique
commentID has unique values Unique
commentSequence has unique values Unique
permID has unique values Unique
recommendations has 7591 (15.2%) zeros Zeros
replyCount has 42417 (85.1%) zeros Zeros

Reproduction

Analysis started2021-11-23 03:15:30.342270
Analysis finished2021-11-23 03:16:11.422607
Duration41.08 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct49865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2488859.417
Minimum80
Maximum4986059
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2021-11-22T21:16:11.567620image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile251669.8
Q11246092
median2489626
Q33723073
95-th percentile4733460.2
Maximum4986059
Range4985979
Interquartile range (IQR)2476981

Descriptive statistics

Standard deviation1435364.497
Coefficient of variation (CV)0.5767157785
Kurtosis-1.19129154
Mean2488859.417
Median Absolute Deviation (MAD)1238450
Skewness0.003078892911
Sum1.241069749 × 1011
Variance2.060271238 × 1012
MonotonicityNot monotonic
2021-11-22T21:16:11.795325image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28931321
 
< 0.1%
22373021
 
< 0.1%
36591851
 
< 0.1%
8506691
 
< 0.1%
11103881
 
< 0.1%
29880851
 
< 0.1%
7062651
 
< 0.1%
1253801
 
< 0.1%
3019511
 
< 0.1%
29828101
 
< 0.1%
Other values (49855)49855
> 99.9%
ValueCountFrequency (%)
801
< 0.1%
1311
< 0.1%
4551
< 0.1%
5001
< 0.1%
5201
< 0.1%
7011
< 0.1%
7071
< 0.1%
7201
< 0.1%
7731
< 0.1%
7861
< 0.1%
ValueCountFrequency (%)
49860591
< 0.1%
49860581
< 0.1%
49859431
< 0.1%
49858921
< 0.1%
49857751
< 0.1%
49855951
< 0.1%
49854811
< 0.1%
49854401
< 0.1%
49851231
< 0.1%
49850821
< 0.1%

commentID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct49865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107599365
Minimum104388915
Maximum110891710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2021-11-22T21:16:12.005021image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum104388915
5-th percentile104723349.2
Q1105992430
median107593218
Q3109183238
95-th percentile110511574.2
Maximum110891710
Range6502795
Interquartile range (IQR)3190808

Descriptive statistics

Standard deviation1851390.306
Coefficient of variation (CV)0.01720633116
Kurtosis-1.187209893
Mean107599365
Median Absolute Deviation (MAD)1594642
Skewness0.0169640153
Sum5.365442333 × 1012
Variance3.427646066 × 1012
MonotonicityNot monotonic
2021-11-22T21:16:12.210247image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1081072001
 
< 0.1%
1072621711
 
< 0.1%
1090952861
 
< 0.1%
1054731351
 
< 0.1%
1058079731
 
< 0.1%
1082205631
 
< 0.1%
1052975821
 
< 0.1%
1045596211
 
< 0.1%
1047953411
 
< 0.1%
1082131861
 
< 0.1%
Other values (49855)49855
> 99.9%
ValueCountFrequency (%)
1043889151
< 0.1%
1043909661
< 0.1%
1043941411
< 0.1%
1043946161
< 0.1%
1043946771
< 0.1%
1043953481
< 0.1%
1043953991
< 0.1%
1043957301
< 0.1%
1043957791
< 0.1%
1043958661
< 0.1%
ValueCountFrequency (%)
1108917101
< 0.1%
1108896211
< 0.1%
1108861261
< 0.1%
1108697551
< 0.1%
1108635491
< 0.1%
1108633711
< 0.1%
1108625471
< 0.1%
1108588231
< 0.1%
1108570151
< 0.1%
1108569391
< 0.1%

status
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
approved
49865 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowapproved
2nd rowapproved
3rd rowapproved
4th rowapproved
5th rowapproved

Common Values

ValueCountFrequency (%)
approved49865
100.0%

Length

2021-11-22T21:16:12.405062image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-22T21:16:12.511737image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
approved49865
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

commentSequence
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct49865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107599365
Minimum104388915
Maximum110891710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2021-11-22T21:16:12.635503image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum104388915
5-th percentile104723349.2
Q1105992430
median107593218
Q3109183238
95-th percentile110511574.2
Maximum110891710
Range6502795
Interquartile range (IQR)3190808

Descriptive statistics

Standard deviation1851390.306
Coefficient of variation (CV)0.01720633116
Kurtosis-1.187209893
Mean107599365
Median Absolute Deviation (MAD)1594642
Skewness0.0169640153
Sum5.365442333 × 1012
Variance3.427646066 × 1012
MonotonicityNot monotonic
2021-11-22T21:16:12.842517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1081072001
 
< 0.1%
1072621711
 
< 0.1%
1090952861
 
< 0.1%
1054731351
 
< 0.1%
1058079731
 
< 0.1%
1082205631
 
< 0.1%
1052975821
 
< 0.1%
1045596211
 
< 0.1%
1047953411
 
< 0.1%
1082131861
 
< 0.1%
Other values (49855)49855
> 99.9%
ValueCountFrequency (%)
1043889151
< 0.1%
1043909661
< 0.1%
1043941411
< 0.1%
1043946161
< 0.1%
1043946771
< 0.1%
1043953481
< 0.1%
1043953991
< 0.1%
1043957301
< 0.1%
1043957791
< 0.1%
1043958661
< 0.1%
ValueCountFrequency (%)
1108917101
< 0.1%
1108896211
< 0.1%
1108861261
< 0.1%
1108697551
< 0.1%
1108635491
< 0.1%
1108633711
< 0.1%
1108625471
< 0.1%
1108588231
< 0.1%
1108570151
< 0.1%
1108569391
< 0.1%

userID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27077
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59882166.51
Minimum1166
Maximum156554620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2021-11-22T21:16:13.053533image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1166
5-th percentile6166787.8
Q144254092
median63636343
Q376607983
95-th percentile104582649.8
Maximum156554620
Range156553454
Interquartile range (IQR)32353891

Descriptive statistics

Standard deviation28824197.61
Coefficient of variation (CV)0.4813486098
Kurtosis0.365014948
Mean59882166.51
Median Absolute Deviation (MAD)15116816
Skewness-0.002691459565
Sum2.986024233 × 1012
Variance8.308343676 × 1014
MonotonicityNot monotonic
2021-11-22T21:16:13.262548image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6789245389
 
0.2%
6678864174
 
0.1%
9308216773
 
0.1%
6893866368
 
0.1%
7296791567
 
0.1%
6348389060
 
0.1%
1284066954
 
0.1%
5867127251
 
0.1%
7835868050
 
0.1%
5187899245
 
0.1%
Other values (27067)49234
98.7%
ValueCountFrequency (%)
11665
< 0.1%
26221
 
< 0.1%
47961
 
< 0.1%
62081
 
< 0.1%
71761
 
< 0.1%
80721
 
< 0.1%
86382
 
< 0.1%
89581
 
< 0.1%
91551
 
< 0.1%
111441
 
< 0.1%
ValueCountFrequency (%)
1565546201
< 0.1%
1564547361
< 0.1%
1561551081
< 0.1%
1561431971
< 0.1%
1560699752
< 0.1%
1560474581
< 0.1%
1560186261
< 0.1%
1559128661
< 0.1%
1555442101
< 0.1%
1553526821
< 0.1%

userDisplayName
Categorical

HIGH CARDINALITY

Distinct20041
Distinct (%)40.2%
Missing16
Missing (%)< 0.1%
Memory size389.7 KiB
John
 
267
David
 
255
Robert
 
182
Mark
 
179
Michael
 
177
Other values (20036)
48789 

Length

Max length203
Median length7
Mean length7.906698229
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13413 ?
Unique (%)26.9%

Sample

1st rowkdw
2nd rowMichael
3rd rowTsumomi
4th rowFilmFan
5th rowAndie

Common Values

ValueCountFrequency (%)
John267
 
0.5%
David255
 
0.5%
Robert182
 
0.4%
Mark179
 
0.4%
Michael177
 
0.4%
Steve176
 
0.4%
Mike163
 
0.3%
Tom144
 
0.3%
Paul144
 
0.3%
Chris142
 
0.3%
Other values (20031)48020
96.3%

Length

2021-11-22T21:16:13.523964image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
john789
 
1.1%
david619
 
0.9%
steve473
 
0.7%
robert438
 
0.6%
michael433
 
0.6%
mark409
 
0.6%
b409
 
0.6%
m408
 
0.6%
paul353
 
0.5%
s336
 
0.5%
Other values (16243)66017
93.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

userLocation
Categorical

HIGH CARDINALITY

Distinct8257
Distinct (%)16.6%
Missing20
Missing (%)< 0.1%
Memory size389.7 KiB
NYC
 
1680
New York
 
1188
California
 
1040
USA
 
694
NY
 
623
Other values (8252)
44620 

Length

Max length49
Median length9
Mean length9.324365533
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4621 ?
Unique (%)9.3%

Sample

1st rowLouisville, KY
2nd rowOttawa
3rd rowPortland, OR
4th rowY’allywood
5th rowGeneva, NY

Common Values

ValueCountFrequency (%)
NYC1680
 
3.4%
New York1188
 
2.4%
California1040
 
2.1%
USA694
 
1.4%
NY623
 
1.2%
Seattle621
 
1.2%
Chicago615
 
1.2%
Boston573
 
1.1%
San Francisco554
 
1.1%
Brooklyn553
 
1.1%
Other values (8247)41704
83.6%

Length

2021-11-22T21:16:13.761854image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new3240
 
4.0%
ny2729
 
3.4%
york2284
 
2.9%
nyc2048
 
2.6%
ca1958
 
2.4%
california1364
 
1.7%
san1309
 
1.6%
nj1109
 
1.4%
usa923
 
1.2%
chicago840
 
1.0%
Other values (4550)62317
77.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

userTitle
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)28.6%
Missing49823
Missing (%)99.9%
Memory size389.7 KiB
The New York Times
14 
editor, Wordplay
10 
Reporter
Columnist
Editorial board writer
Other values (7)

Length

Max length34
Median length16
Mean length16
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)16.7%

Sample

1st rowThe New York Times
2nd rowAssistant Editor, Interactive News
3rd rowThe New York Times
4th rowReporter
5th rowReporter

Common Values

ValueCountFrequency (%)
The New York Times14
 
< 0.1%
editor, Wordplay10
 
< 0.1%
Reporter6
 
< 0.1%
Columnist3
 
< 0.1%
Editorial board writer2
 
< 0.1%
Assistant Editor, Interactive News1
 
< 0.1%
Science reporter1
 
< 0.1%
Contributor, former Times reporter1
 
< 0.1%
Foreign Correspondent1
 
< 0.1%
Editor1
 
< 0.1%
Other values (2)2
 
< 0.1%
(Missing)49823
99.9%

Length

2021-11-22T21:16:13.951261image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
times15
13.9%
the14
13.0%
york14
13.0%
new14
13.0%
editor13
12.0%
wordplay10
9.3%
reporter8
7.4%
columnist3
 
2.8%
editorial2
 
1.9%
board2
 
1.9%
Other values (12)13
12.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

commentBody
Categorical

HIGH CARDINALITY
UNIFORM

Distinct49849
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
Thank you.
 
4
Well said.
 
3
Brilliant!
 
3
Agreed.
 
2
No.
 
2
Other values (49844)
49851 

Length

Max length2592
Median length282
Mean length388.06666
Min length3

Characters and Unicode

Total characters82661
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49837 ?
Unique (%)99.9%

Sample

1st row@Jo Williams Exactly I see a nightmarish Gotham City for all of USA with no Batman or superhero to help save us.
2nd row@Bugmon Are you really that obtuse? I do wear a mask when I go out. My point is that wearing a mask is not a 100% guarantee against spreading infection.
3rd rowIt's so sad to see Trump's impact on our nation. This week's war he tried to start, rolling back environmental laws, and opening up national parks to oil exploration, the impeachable offenses he and his criminal friends committed, and all his continuous bluffing and luing. It's almost too much to listen to or watch or read. I agree with the article, he is bringing down the nation to his level, which is mentally ill, and certainly not very smart. I looked up Vegas bets tonight about his probability of winning the next election, and they all seem to think he's the man. Tragic for our nation if it turns out to be true. I so wish our nation was on a brighter path.
4th rowThe children’s hospital in my city has enormous cash reserves and is constantly building urgent care centers, new hospital wings, etc. I realize it is unpopular to criticize a children’s hospital but I find it utterly ridiculous that so many women in my community donate untold thousands of hours hosting charity events and decorating mailbox wreaths to support this hospital that sits on millions in cash. Do they realize this money is not going directly to children and doctors and nurses but likely towards executive salaries for even more fundraising and buildings? Friends who work at the hospital in management have told me they are unwilling to work with or share any resources with other hospitals. Toxic fiefdoms and culture are rampant. Healthcare is a greedy business even for children and it needs to change.
5th rowI can't wait to watch this virtually with other folks. My grandmother introduced me to Cary Grant's films when I was just a young girl and I soon landed on this classic as my favorite. Rosalind Russell is a joy to watch and certainly is in the top 3 of Grant's co-stars in keeping up with him physically and verbally. If this movie isn't in Aaron Sorkin's cinematic DNA, I'd be shocked. Happy viewing!

Common Values

ValueCountFrequency (%)
Thank you.4
 
< 0.1%
Well said.3
 
< 0.1%
Brilliant!3
 
< 0.1%
Agreed.2
 
< 0.1%
No.2
 
< 0.1%
Excellent!2
 
< 0.1%
Me too.2
 
< 0.1%
Amen.2
 
< 0.1%
YES!2
 
< 0.1%
Agreed2
 
< 0.1%
Other values (49839)49841
> 99.9%

Length

2021-11-22T21:16:14.156276image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the167084
 
5.0%
to97261
 
2.9%
and89968
 
2.7%
of76870
 
2.3%
a71414
 
2.1%
is54279
 
1.6%
in52772
 
1.6%
that46687
 
1.4%
i37614
 
1.1%
for34682
 
1.0%
Other values (82528)2598680
78.1%

Most occurring characters

ValueCountFrequency (%)
82661
100.0%

Most occurring categories

ValueCountFrequency (%)
Control82661
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
82661
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common82661
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
82661
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII82661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
82661
100.0%

createDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct49803
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
2020-02-16 16:51:10
 
2
2020-09-22 11:21:05
 
2
2020-09-09 21:09:28
 
2
2020-11-13 17:25:46
 
2
2020-07-14 15:25:37
 
2
Other values (49798)
49855 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49741 ?
Unique (%)99.8%

Sample

1st row2020-07-13 15:33:55
2nd row2020-06-23 01:09:24
3rd row2020-01-10 06:21:20
4th row2020-06-08 15:03:28
5th row2020-04-03 15:25:21

Common Values

ValueCountFrequency (%)
2020-02-16 16:51:102
 
< 0.1%
2020-09-22 11:21:052
 
< 0.1%
2020-09-09 21:09:282
 
< 0.1%
2020-11-13 17:25:462
 
< 0.1%
2020-07-14 15:25:372
 
< 0.1%
2020-05-29 12:56:552
 
< 0.1%
2020-06-10 15:42:532
 
< 0.1%
2020-12-12 04:12:532
 
< 0.1%
2020-01-02 14:20:402
 
< 0.1%
2020-10-23 13:59:272
 
< 0.1%
Other values (49793)49845
> 99.9%

Length

2021-11-22T21:16:14.335705image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-03-04248
 
0.2%
2020-03-24246
 
0.2%
2020-07-08240
 
0.2%
2020-02-12237
 
0.2%
2020-02-05236
 
0.2%
2020-03-11231
 
0.2%
2020-07-07230
 
0.2%
2020-04-08230
 
0.2%
2020-03-05228
 
0.2%
2020-04-01226
 
0.2%
Other values (35797)97378
97.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

updateDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct49793
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
2020-10-28 15:09:11
 
2
2020-04-02 15:12:57
 
2
2020-04-03 01:47:20
 
2
2020-08-04 13:28:14
 
2
2020-06-18 23:04:34
 
2
Other values (49788)
49855 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49721 ?
Unique (%)99.7%

Sample

1st row2020-07-14 16:47:04
2nd row2020-06-25 10:27:26
3rd row2020-01-11 20:01:58
4th row2020-06-09 15:40:03
5th row2020-04-05 04:31:06

Common Values

ValueCountFrequency (%)
2020-10-28 15:09:112
 
< 0.1%
2020-04-02 15:12:572
 
< 0.1%
2020-04-03 01:47:202
 
< 0.1%
2020-08-04 13:28:142
 
< 0.1%
2020-06-18 23:04:342
 
< 0.1%
2020-09-16 05:13:592
 
< 0.1%
2020-06-08 04:44:112
 
< 0.1%
2020-07-29 13:27:562
 
< 0.1%
2020-03-11 17:36:582
 
< 0.1%
2020-04-26 20:43:102
 
< 0.1%
Other values (49783)49845
> 99.9%

Length

2021-11-22T21:16:14.478719image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-06-02219
 
0.2%
2020-03-11214
 
0.2%
2020-02-13211
 
0.2%
2020-02-05210
 
0.2%
2020-07-07206
 
0.2%
2020-02-06205
 
0.2%
2020-02-12204
 
0.2%
2020-04-16202
 
0.2%
2020-03-04200
 
0.2%
2020-07-08198
 
0.2%
Other values (36728)97661
97.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

approveDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct49237
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
2020-04-09 15:39:56
 
3
2020-09-19 19:49:17
 
3
2020-03-01 18:30:44
 
3
2021-01-01 01:15:49
 
3
2020-04-21 11:54:57
 
3
Other values (49232)
49850 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48616 ?
Unique (%)97.5%

Sample

1st row2020-07-13 16:11:46
2nd row2020-06-23 01:27:44
3rd row2020-01-10 06:21:22
4th row2020-06-08 15:03:29
5th row2020-04-03 15:25:22

Common Values

ValueCountFrequency (%)
2020-04-09 15:39:563
 
< 0.1%
2020-09-19 19:49:173
 
< 0.1%
2020-03-01 18:30:443
 
< 0.1%
2021-01-01 01:15:493
 
< 0.1%
2020-04-21 11:54:573
 
< 0.1%
2020-03-26 20:25:323
 
< 0.1%
2020-04-09 15:32:073
 
< 0.1%
2020-02-01 15:47:382
 
< 0.1%
2020-03-05 13:12:012
 
< 0.1%
2020-02-27 04:11:222
 
< 0.1%
Other values (49227)49838
99.9%

Length

2021-11-22T21:16:14.631696image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-02-05246
 
0.2%
2020-03-04234
 
0.2%
2020-02-12234
 
0.2%
2020-03-24233
 
0.2%
2020-07-07232
 
0.2%
2020-06-01229
 
0.2%
2020-03-11228
 
0.2%
2020-02-13226
 
0.2%
2020-03-05225
 
0.2%
2020-07-08222
 
0.2%
Other values (34460)97421
97.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recommendations
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct673
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.60366991
Minimum0
Maximum5238
Zeros7591
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2021-11-22T21:16:14.800360image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q312
95-th percentile62
Maximum5238
Range5238
Interquartile range (IQR)11

Descriptive statistics

Standard deviation108.7872359
Coefficient of variation (CV)5.279993145
Kurtosis623.3599352
Mean20.60366991
Median Absolute Deviation (MAD)4
Skewness20.49125599
Sum1027402
Variance11834.66269
MonotonicityNot monotonic
2021-11-22T21:16:15.267223image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07591
15.2%
16144
12.3%
24809
 
9.6%
33925
 
7.9%
43180
 
6.4%
52473
 
5.0%
62115
 
4.2%
71734
 
3.5%
81462
 
2.9%
91298
 
2.6%
Other values (663)15134
30.3%
ValueCountFrequency (%)
07591
15.2%
16144
12.3%
24809
9.6%
33925
7.9%
43180
6.4%
52473
 
5.0%
62115
 
4.2%
71734
 
3.5%
81462
 
2.9%
91298
 
2.6%
ValueCountFrequency (%)
52381
< 0.1%
51531
< 0.1%
48051
< 0.1%
43481
< 0.1%
38041
< 0.1%
37881
< 0.1%
36131
< 0.1%
35141
< 0.1%
35051
< 0.1%
33961
< 0.1%

replyCount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct61
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4413315953
Minimum0
Maximum117
Zeros42417
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2021-11-22T21:16:15.463237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum117
Range117
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.28637511
Coefficient of variation (CV)5.180628656
Kurtosis519.2815059
Mean0.4413315953
Median Absolute Deviation (MAD)0
Skewness17.7089783
Sum22007
Variance5.227511142
MonotonicityNot monotonic
2021-11-22T21:16:15.670256image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
042417
85.1%
13737
 
7.5%
21542
 
3.1%
3724
 
1.5%
4421
 
0.8%
5260
 
0.5%
6165
 
0.3%
7109
 
0.2%
890
 
0.2%
955
 
0.1%
Other values (51)345
 
0.7%
ValueCountFrequency (%)
042417
85.1%
13737
 
7.5%
21542
 
3.1%
3724
 
1.5%
4421
 
0.8%
5260
 
0.5%
6165
 
0.3%
7109
 
0.2%
890
 
0.2%
955
 
0.1%
ValueCountFrequency (%)
1171
< 0.1%
1111
< 0.1%
891
< 0.1%
781
< 0.1%
701
< 0.1%
671
< 0.1%
631
< 0.1%
612
< 0.1%
602
< 0.1%
571
< 0.1%

editorsSelection
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
False
49267 
True
 
598
ValueCountFrequency (%)
False49267
98.8%
True598
 
1.2%
2021-11-22T21:16:15.805789image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

parentID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21150
Distinct (%)98.0%
Missing28290
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean107552394
Minimum104390618
Maximum110879849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2021-11-22T21:16:15.941610image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum104390618
5-th percentile104728508.1
Q1105936971.5
median107514634
Q3109100114
95-th percentile110497977.2
Maximum110879849
Range6489231
Interquartile range (IQR)3163142.5

Descriptive statistics

Standard deviation1842000.367
Coefficient of variation (CV)0.0171265399
Kurtosis-1.178731317
Mean107552394
Median Absolute Deviation (MAD)1580151
Skewness0.05460173987
Sum2.3204429 × 1012
Variance3.392965351 × 1012
MonotonicityNot monotonic
2021-11-22T21:16:16.156831image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1046782004
 
< 0.1%
1082517083
 
< 0.1%
1077152313
 
< 0.1%
1056563753
 
< 0.1%
1077524883
 
< 0.1%
1103852873
 
< 0.1%
1094115983
 
< 0.1%
1093859553
 
< 0.1%
1084381323
 
< 0.1%
1084064153
 
< 0.1%
Other values (21140)21544
43.2%
(Missing)28290
56.7%
ValueCountFrequency (%)
1043906181
< 0.1%
1043939881
< 0.1%
1043939901
< 0.1%
1043940071
< 0.1%
1043942682
< 0.1%
1043961581
< 0.1%
1043962901
< 0.1%
1043963791
< 0.1%
1043965991
< 0.1%
1043967611
< 0.1%
ValueCountFrequency (%)
1108798491
< 0.1%
1108791961
< 0.1%
1108586601
< 0.1%
1108552961
< 0.1%
1108507701
< 0.1%
1108481121
< 0.1%
1108422451
< 0.1%
1108407191
< 0.1%
1108396261
< 0.1%
1108392421
< 0.1%

parentUserDisplayName
Categorical

HIGH CARDINALITY
MISSING

Distinct10037
Distinct (%)46.5%
Missing28299
Missing (%)56.8%
Memory size389.7 KiB
John
 
147
Doug
 
147
David
 
117
Mark
 
108
Mike
 
108
Other values (10032)
20939 

Length

Max length48
Median length7
Mean length7.846703144
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6943 ?
Unique (%)32.2%

Sample

1st rowJo Williams
2nd rowMIKEinNYC
3rd rowstan continople
4th rowEGD
5th rowTom B.

Common Values

ValueCountFrequency (%)
John147
 
0.3%
Doug147
 
0.3%
David117
 
0.2%
Mark108
 
0.2%
Mike108
 
0.2%
Paul92
 
0.2%
Michael89
 
0.2%
Socrates85
 
0.2%
Chris78
 
0.2%
Steve67
 
0.1%
Other values (10027)20528
41.2%
(Missing)28299
56.8%

Length

2021-11-22T21:16:16.384846image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
john399
 
1.3%
david303
 
1.0%
mark231
 
0.8%
michael209
 
0.7%
b199
 
0.6%
m196
 
0.6%
paul192
 
0.6%
mike187
 
0.6%
doug174
 
0.6%
j172
 
0.6%
Other values (8742)28396
92.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

depth
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
1
28290 
2
21575 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
128290
56.7%
221575
43.3%

Length

2021-11-22T21:16:16.553765image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-22T21:16:16.660583image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
128290
56.7%
221575
43.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

commentType
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
comment
28289 
userReply
21534 
reporterReply
 
42

Length

Max length13
Median length7
Mean length7.868745613
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuserReply
2nd rowuserReply
3rd rowcomment
4th rowcomment
5th rowcomment

Common Values

ValueCountFrequency (%)
comment28289
56.7%
userReply21534
43.2%
reporterReply42
 
0.1%

Length

2021-11-22T21:16:16.775460image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-22T21:16:16.885471image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
comment28289
56.7%
userreply21534
43.2%
reporterreply42
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

trusted
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
0
49865 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
049865
100.0%

Length

2021-11-22T21:16:16.995476image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-22T21:16:17.091490image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
049865
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recommendedFlag
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
0
49865 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
049865
100.0%

Length

2021-11-22T21:16:17.175491image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-22T21:16:17.277498image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
049865
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

permID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct49865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107599365
Minimum104388915
Maximum110891710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2021-11-22T21:16:17.392299image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum104388915
5-th percentile104723349.2
Q1105992430
median107593218
Q3109183238
95-th percentile110511574.2
Maximum110891710
Range6502795
Interquartile range (IQR)3190808

Descriptive statistics

Standard deviation1851390.306
Coefficient of variation (CV)0.01720633116
Kurtosis-1.187209893
Mean107599365
Median Absolute Deviation (MAD)1594642
Skewness0.0169640153
Sum5.365442333 × 1012
Variance3.427646066 × 1012
MonotonicityNot monotonic
2021-11-22T21:16:17.599230image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1081072001
 
< 0.1%
1072621711
 
< 0.1%
1090952861
 
< 0.1%
1054731351
 
< 0.1%
1058079731
 
< 0.1%
1082205631
 
< 0.1%
1052975821
 
< 0.1%
1045596211
 
< 0.1%
1047953411
 
< 0.1%
1082131861
 
< 0.1%
Other values (49855)49855
> 99.9%
ValueCountFrequency (%)
1043889151
< 0.1%
1043909661
< 0.1%
1043941411
< 0.1%
1043946161
< 0.1%
1043946771
< 0.1%
1043953481
< 0.1%
1043953991
< 0.1%
1043957301
< 0.1%
1043957791
< 0.1%
1043958661
< 0.1%
ValueCountFrequency (%)
1108917101
< 0.1%
1108896211
< 0.1%
1108861261
< 0.1%
1108697551
< 0.1%
1108635491
< 0.1%
1108633711
< 0.1%
1108625471
< 0.1%
1108588231
< 0.1%
1108570151
< 0.1%
1108569391
< 0.1%

isAnonymous
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
False
49865 
ValueCountFrequency (%)
False49865
100.0%
2021-11-22T21:16:17.742237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

articleID
Categorical

HIGH CARDINALITY

Distinct9448
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75
 
87
nyt://article/57736deb-c3d4-5640-8732-bce6442e16c4
 
70
nyt://article/b316ddae-bf03-5de8-b9e6-a796d0471430
 
52
nyt://article/b9c6769e-e64b-5c2c-8be1-6352c3bb6520
 
50
nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c7
 
48
Other values (9443)
49558 

Length

Max length54
Median length50
Mean length50.09714228
Min length50

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2800 ?
Unique (%)5.6%

Sample

1st rownyt://article/ceee4614-2a34-5964-afa8-e6c1aa2ce993
2nd rownyt://article/5d10b98f-b66f-5bfc-9bd2-321abe8b8f3d
3rd rownyt://article/1e5a3172-e5ed-57ae-824e-a8d6c974b696
4th rownyt://article/8fc3fae7-64dc-5717-a2e7-0ad507dce6aa
5th rownyt://article/dd05a4be-2e90-5fc2-ab6d-1423b8d9188a

Common Values

ValueCountFrequency (%)
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d7587
 
0.2%
nyt://article/57736deb-c3d4-5640-8732-bce6442e16c470
 
0.1%
nyt://article/b316ddae-bf03-5de8-b9e6-a796d047143052
 
0.1%
nyt://article/b9c6769e-e64b-5c2c-8be1-6352c3bb652050
 
0.1%
nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c748
 
0.1%
nyt://article/d35b554e-eda8-505f-80b0-3e9b9344ecd148
 
0.1%
nyt://article/de217dd9-3383-574a-9eaf-c0303570e79447
 
0.1%
nyt://article/032c8257-cfe7-5e69-8042-4bfe2b13186f47
 
0.1%
nyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aaf46
 
0.1%
nyt://article/abd7cf55-3363-5c57-9f25-734d72ce653045
 
0.1%
Other values (9438)49325
98.9%

Length

2021-11-22T21:16:17.840246image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d7587
 
0.2%
nyt://article/57736deb-c3d4-5640-8732-bce6442e16c470
 
0.1%
nyt://article/b316ddae-bf03-5de8-b9e6-a796d047143052
 
0.1%
nyt://article/b9c6769e-e64b-5c2c-8be1-6352c3bb652050
 
0.1%
nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c748
 
0.1%
nyt://article/d35b554e-eda8-505f-80b0-3e9b9344ecd148
 
0.1%
nyt://article/de217dd9-3383-574a-9eaf-c0303570e79447
 
0.1%
nyt://article/032c8257-cfe7-5e69-8042-4bfe2b13186f47
 
0.1%
nyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aaf46
 
0.1%
nyt://article/0525de83-8774-5f76-8d16-5b8e08a71b9245
 
0.1%
Other values (9438)49325
98.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-11-22T21:16:07.010017image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2021-11-22T21:16:07.221410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2021-11-22T21:16:06.588260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2021-11-22T21:16:18.006260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-22T21:16:18.300282image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-22T21:16:18.587451image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-22T21:16:18.859474image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-22T21:16:19.349168image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-22T21:16:08.943129image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-22T21:16:10.019078image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-22T21:16:10.588129image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-22T21:16:10.946199image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexcommentIDstatuscommentSequenceuserIDuserDisplayNameuserLocationuserTitlecommentBodycreateDateupdateDateapproveDaterecommendationsreplyCounteditorsSelectionparentIDparentUserDisplayNamedepthcommentTypetrustedrecommendedFlagpermIDisAnonymousarticleID
02893132108107200approved10810720022178100kdwLouisville, KYNaN@Jo Williams Exactly I see a nightmarish Gotham City for all of USA with no Batman or superhero to help save us.2020-07-13 15:33:552020-07-14 16:47:042020-07-13 16:11:4620False108103586.0Jo Williams2userReply00108107200Falsenyt://article/ceee4614-2a34-5964-afa8-e6c1aa2ce993
12599853107731134approved10773113455663429MichaelOttawaNaN@Bugmon \n\nAre you really that obtuse?\n\nI do wear a mask when I go out.\n\nMy point is that wearing a mask is not a 100% guarantee against spreading infection.2020-06-23 01:09:242020-06-25 10:27:262020-06-23 01:27:4410False107730611.0MIKEinNYC2userReply00107731134Falsenyt://article/5d10b98f-b66f-5bfc-9bd2-321abe8b8f3d
2125068104552271approved10455227161505299TsumomiPortland, ORNaNIt's so sad to see Trump's impact on our nation. This week's war he tried to start, rolling back environmental laws, and opening up national parks to oil exploration, the impeachable offenses he and his criminal friends committed, and all his continuous bluffing and luing. It's almost too much to listen to or watch or read. I agree with the article, he is bringing down the nation to his level, which is mentally ill, and certainly not very smart. I looked up Vegas bets tonight about his probability of winning the next election, and they all seem to think he's the man. Tragic for our nation if it turns out to be true. I so wish our nation was on a brighter path.2020-01-10 06:21:202020-01-11 20:01:582020-01-10 06:21:22441FalseNaNNaN1comment00104552271Falsenyt://article/1e5a3172-e5ed-57ae-824e-a8d6c974b696
32425848107493366approved10749336655596960FilmFanY’allywoodNaNThe children’s hospital in my city has enormous cash reserves and is constantly building urgent care centers, new hospital wings, etc. I realize it is unpopular to criticize a children’s hospital but I find it utterly ridiculous that so many women in my community donate untold thousands of hours hosting charity events and decorating mailbox wreaths to support this hospital that sits on millions in cash. Do they realize this money is not going directly to children and doctors and nurses but likely towards executive salaries for even more fundraising and buildings? Friends who work at the hospital in management have told me they are unwilling to work with or share any resources with other hospitals. Toxic fiefdoms and culture are rampant. Healthcare is a greedy business even for children and it needs to change.2020-06-08 15:03:282020-06-09 15:40:032020-06-08 15:03:29781FalseNaNNaN1comment00107493366Falsenyt://article/8fc3fae7-64dc-5717-a2e7-0ad507dce6aa
41444275106241790approved10624179068597593AndieGeneva, NYNaNI can't wait to watch this virtually with other folks. My grandmother introduced me to Cary Grant's films when I was just a young girl and I soon landed on this classic as my favorite. Rosalind Russell is a joy to watch and certainly is in the top 3 of Grant's co-stars in keeping up with him physically and verbally. If this movie isn't in Aaron Sorkin's cinematic DNA, I'd be shocked. \nHappy viewing!2020-04-03 15:25:212020-04-05 04:31:062020-04-03 15:25:2281FalseNaNNaN1comment00106241790Falsenyt://article/dd05a4be-2e90-5fc2-ab6d-1423b8d9188a
51737257106608296approved10660829642936585Nancy GMANaNDoesn't he know the virus is already here??2020-04-21 14:13:382020-04-21 15:05:322020-04-21 14:31:5160FalseNaNNaN1comment00106608296Falsenyt://article/3e52c8d2-b72d-5fd0-872d-d6b69e86affc
62803067107989580approved107989580107693923bilbeonew york, nyNaN@stan continople In Greenpoint as well and wondered the exact same thing as I wandered around Sunday to the waterfront and into Williamsburg. I felt so jealous for a moment to think about how good it would be to have that kind of money! These buildings are like nothing I have ever seen before. The area is alien to me; totally different from when I came here 13 years ago.2020-07-07 14:38:522020-07-08 16:24:262020-07-07 14:38:53170False107986742.0stan continople2userReply00107989580Falsenyt://article/1eeac815-17ff-5493-967b-6eda132d5b00
73612604109029854approved10902985466072150Johnny PanicBurlington, MassachusettsNaN@EGD Oh boy, here we go again, that tired and well debunked John Birch Society originated "We are a constitutional republic, not a democracy" trope. It's a shame civics classes aren't widespread anymore, and it's sad when people who did take civics classes in school forget and actually argue against everything they were taught.2020-09-09 13:50:332020-09-09 22:44:542020-09-09 14:14:2320False109022963.0EGD2userReply00109029854Falsenyt://article/25f302f9-34cb-5ea7-bdce-990af0303d2a
82906211108124788approved10812478897953061Dar JamesPANaNRugged Individualism.\n\nWhat would Jesus do?2020-07-14 15:00:532020-07-14 17:41:582020-07-14 17:41:5800FalseNaNNaN1comment00108124788Falsenyt://article/df1f79af-d370-5432-a384-8e61094c2f0d
92532219107641499approved10764149998563280Paul-ASt. Lawrence, NYNaN@Tom B. \nBesides the reasons other people have cited, there are other factors which could possibly be factors:\n\n- There are different strains of the virus. Some are more lethal than others.\n\n- There are other health factors which increase the death rates (e.g. living in polluted areas, other health conditions, etc.)\n\n- There's some evidence of correlation with factors such as blood type, racial background, etc. These might not prove to be direct correlations (and are not causations); but they could be part of the complicated picture.\n\nBut until more info is known, it appears that the time lag between diagnosis and death that many others have commented about is a plausible explanation of these early numbers.2020-06-17 02:33:272020-06-17 05:43:572020-06-17 02:33:2910False107639368.0Tom B.2userReply00107641499Falsenyt://article/2ec11b93-6f51-5107-9a9c-526d2cb72ea4

Last rows

df_indexcommentIDstatuscommentSequenceuserIDuserDisplayNameuserLocationuserTitlecommentBodycreateDateupdateDateapproveDaterecommendationsreplyCounteditorsSelectionparentIDparentUserDisplayNamedepthcommentTypetrustedrecommendedFlagpermIDisAnonymousarticleID
498552747271107925354approved10792535429823394BokmalUSANaN@Grace Important points. I have asthma and masks excerbate it. And I have tried a range of masks. More enlightened countries, like Canada, do not require those with lung disease, to wear masks.2020-07-02 17:38:192020-07-02 17:38:202020-07-02 17:38:2000False107913416.0Rith2userReply00107925354Falsenyt://article/b661b5b0-95b5-55d9-8be6-d2b91d11897a
498564921862110753692approved11075369271180810Marcos CamposNew YorkNaNWhat we have had all along is a juvenile know-nothing mentally unstable misfit in the White House and a political party that has seen better days, having anchored their future to a fraud and a criminal.\n\nLoeffler and Purdue hitched their wagons to this madman and now he's thrown them and GOP leadership under the bus.2020-12-24 15:01:582020-12-25 20:42:192020-12-24 15:56:31280FalseNaNNaN1comment00110753692Falsenyt://article/8ee8d140-27f2-57b4-a39a-1a4fec1ddc6e
498572206339107218445approved10721844553630318StevePortland, MENaN@richard wiesner \nBravo! I sincerely hope that parents come to realize that teaching is a very challenging job, and paying for quality teachers is worth every cent. Plus, I also hope that people come to realize that lowering class sizes will greatly improve educational experience as well as help contain the spread of disease in the future. Most importantly, I hope parents start listening to teachers, and not listen to politicians and businesspersons for insights about education.2020-05-25 10:25:222020-08-12 18:25:082020-05-25 10:25:23420False107217555.0richard wiesner2userReply00107218445Falsenyt://article/26c2e73c-1bf5-500e-bf00-be76e69087b2
49858901071105558311approved10555831148701991heidiwriterPacific Palisades, CANaN@Mary, except conviction. Oh, but wait, you don't seem to believe such a thing is possible...2020-03-02 20:18:442020-03-02 21:53:002020-03-02 21:53:0000False105556632.0JH2userReply00105558311Falsenyt://article/b14a3c25-8975-54e2-9d0c-a47abb0fe56f
498592111635107094087approved10709408777112720RobertaKansas CityNaN@Devil's Advocate I'm a school psychologist with my training primarily in cognitive psychology. The "verbal gaffes" that Biden occasionally struggles with can most definitely a symptom of a mild speech impediment such as stuttering. \nI see this all the time with the kids I've worked with -- their verbal reasoning may show a deficit in that area, while their abilities are often above average in other cognitive areas, such as nonverbal reasoning and fluid reasoning, executive functioning, processing information speed, working memory, etc. I believe this to be the case with Joe Biden. \nTrump, on the hand ... well that's an entirely different story. Trump is a raving, unhinged lunatic who should not be anywhere near the nuclear codes.2020-05-17 18:08:352020-05-20 23:55:142020-05-17 18:27:57260False107091755.0Glen2userReply00107094087Falsenyt://article/0457a10e-6ae9-584f-aa93-e49474764a7e
498603009694108244250approved10824425011967380mutineerGeneva, NYNaNBest last sentence in any column, ever.2020-07-21 12:25:272020-07-21 21:28:092020-07-21 20:50:5710FalseNaNNaN1comment00108244250Falsenyt://article/d3913502-4cc0-54ce-8372-8e98105ba9fd
49861592853105155962approved10515596280110445pimaineNaNRoger Stone, along with Paul Manafort and Lee Atwater transformed the once respected GOP into the Republican party of dirty tricks as we know it today. \n\nTheir co conspirator Newt Gingrich begat Mitch McConnell. Before there was lying Donald Trump, there were Tricky Dick Nixon, genial vicious fabulist Ronald Reagan, and cherry picking war mongering Bush, Cheney, and Rumsfeld. Lies and more lies. Given the imprimatur of gospel truth by the religious right.\n\nBefore Donald Trump pardoned military war criminals, Bill Barr was getting Reagan's Iran Contra operatives off the hook.\n\nIts not the 'deep state', its the old boys club. Its the same players - right down to Donald Trump's mentor Roy Cohn at the intersection of Joe McCarthy and the Mob - in the tragedy of America's downfall playing out center stage.2020-02-11 20:54:332020-02-13 20:40:022020-02-11 23:33:521381FalseNaNNaN1comment00105155962Falsenyt://article/da5df871-c799-5619-b9a0-ab6e4c8a0533
498621359418106116945approved10611694559919476CharlesM1950Austin TXNaNNo one individual tweets 798 times in a day. It’s either a bot or a team. In either case the intent is to leverage the pandemic for the purpose of creating division in our country. I smell a rat, a rat named Putin. I also smell a send rat named Trump because he doesn’t actively separate himself from this divisive propaganda. When the Trump Presidency ends I am certain we will find a trail of droppings that lead straight to the Kremlin.2020-03-29 03:03:002020-03-30 14:09:442020-03-29 16:40:2160FalseNaNNaN1comment00106116945Falsenyt://article/bd4b1fad-9b89-5d1e-95ed-301406ea4b65
498634886496110704822approved11070482266864730Al MNorfolk VaNaN@Anthony \n\nOr the lives of journalists and civilians?2020-12-21 14:48:162020-12-21 23:13:552020-12-21 17:52:0210False110704720.0Anthony2userReply00110704822Falsenyt://article/e4dcbe3f-3182-52f1-b1d2-fb946ef7d4cd
49864463789105027047approved10502704798839260KopecGlenbard West, Glen Ellyn, ILNaNI believe Senator Klobuchar’s act of sharing a homemade hot dish recipe was a smart move when it comes to connecting with voters. Food is the ultimate connector, everyone needs food to survive and grow; and the act of eating food releases happy chemicals into our brains. Not only did Ms. Klobuchar share a friendly, open side of herself she has shown her commonalities with regular people. When one would regularly imagine a president the image of that president laboring away in the kitchen on a modest meal is not one the usually comes to mind. In many ways this act was “thinking outside the box” in a way that expressed senator Klobuchar true self to those who are going to be deciding the next leader of the free world. So in the age of increasing “fake ness” and media projections becoming reality I applaud a very real lady.2020-02-05 16:40:112020-02-05 16:40:302020-02-05 16:40:3000FalseNaNNaN1comment00105027047Falsenyt://article/725a382f-a86d-5af3-8899-acd4e805487a